The computational modeling of human visual attention has received much attention in recent decades. In advanced industrial applications, it has been demonstrated that computational visual attention models (CVAMs) can predict visual attention very similarly to human visual attention. However, it is controversial whether the driver’s eye fixation location (EFL) or the predicted eye fixation location of computational visual attention models is more reliable and helpful for actual driving. To address this issue, an open database of videos taken under the most common 18 driving conditions in everyday driving has been established. In experiments using this database, expert drivers found that it was not sufficient for drivers to rely on only one of the two EFLs. Based on this finding, a hybrid EFL recommendation strategy is proposed for improving driving safety. By extracting visual characteristics from human dynamic vision, the performance of the proposed recommendation method demonstrates its potential value in these collected driving tasks. In addition, the visual comfort of driving is further addressed to enhance the safety of driving. From the results of experiments on 108 driving video clips taken of the most common 18 real driving conditions, it is confirmed that the proposed EFL recommendation achieves an experience rating of driving comfort between 88.1 and 92.7 out of 100.

Abnormal driving may cause serious danger to both the driver and the public. Existing detectors of abnormal driving behavior are mainly based on shallow models, which require large quantities of labeled data. The acquisition and labelling of abnormal driving data are, however, difficult, labor-intensive and time-consuming. This situation inspires us to rethink the abnormal driving detection problem and to apply deep architecture models. In this study, we establish a novel deep-learning-based model for abnormal driving detection. A stacked sparse autoencoders model is used to learn generic driving behavior features. The model is trained in a greedy layer-wise fashion. As far as the authors know, this is the first time that a deep learning approach is applied using autoencoders as building blocks to represent driving features for abnormal driving detection. In addition, a method for denoising is added to the algorithm to increase the robustness of feature expression. The dropout technology is introduced into the entire training process to avoid overfitting. Experiments carried out on our self-created driving behavior dataset demonstrate that the proposed scheme achieves a superior performance for abnormal driving detection compared to the state-of-the-art.

To improve the precision of defect categorization and localization in images, this paper proposes an approach for detecting surface defects in hot-rolled steel strips. The approach uses an improved YOLOv5 network model to overcome the issues of inadequate feature extraction capacity and suboptimal feature integration when identifying surface defects on steel strips. The proposed method achieves higher detection accuracy and localization precision, making it more competitive and applicable in real production. Firstly, the multi-scale feature fusion (MSF) strategy is utilized to fuse shallow and deep features effectively and enrich detailed information relevant to target defects. Secondly, the CSPLayer Res2Attention block (CRA block) residual module is introduced to reduce the loss of defect information during hierarchical transmission, thereby enhancing the extraction of fine-grained features and improving the perception of details and global features. Finally, the experimental results indicate that the mAP on the NEU-DET and GC10-DET datasets approaches 78.5% and 67.3%, respectively, which is 4.9% and 2.1% higher than that of the baseline. Meanwhile, it has higher precision and more precise localization capabilities than other methods. Furthermore, it also achieves 59.2% mAP on the APDDD dataset, indicating its potential for growth in further domains.

Many studies have been conducted on single lane detection, but multi-lane detection is rarely addressed. The latter is more advantageous for applications such as autonomous navigation, unmanned vehicles, departure warning, and cruise control. In this paper, we propose a novel and robust multiple lane detection algorithm based on the road structure information, which contains five complementary constraints: length constraint, parallel constraint, distribution constraint, pair constraint and uniform width constraint. All the five constraints are incorporated into a Hough transform (HT) based unified framework to select lane candidates. Nearly 99% of the false alarm candidates in HT space can be removed. Moreover, a dynamic programming strategy is proposed to find the most rational solutions among the remaining candidates. This strategy can effectively deal with combination complexity and interferences introduced by multi-lane detection. Experimental results on the benchmark dataset and other collected data demonstrate that the proposed method can outperform the state-of-the-art approaches in both accuracy and efficiency.

The infrared small target detection (IRSTD) task presents significant challenges due to low signal-to-clutter ratio, complicated background, and strong interferences. While tensor theory has shown promise in detection performance, three issues regarding damaged tensor construction, inaccurate tensor models, and high computation complexity remain. This study addresses these issues by introducing an independent spatial-temporal perspective, and proposes a fast and separable spatial-temporal tensor completion model. A new tensor structure named separable spatial-temporal patch-tensor pair (SSPP) is conceived to alleviate the dilemma of maintaining neighborhood structure and temporal consistency when constructing image tensors. By treating spatial and temporal dimensions as independent, SSPP enables flexible distribution hypotheses and representations in each dimension. Two tensor models are devised: the spatial model focuses on target enhancement in the spatial dimension, while the temporal one concentrates on suppressing strong interference in the temporal dimension. A long-term memory regularization is further introduced to the temporal model for target movement perception, enhancing its robustness to interferences. By combining these tensor models and employing a coarse-to-fine detection strategy, our method offers an effective solution for IRSTD. Extensive experiments on practical datasets have demonstrated the superiority of the proposed method in terms of target enhancement, background suppression, and detection efficiency.

Visible–infrared person reidentification (VI-ReID) aims to search for pedestrian identities in different spectra. The major challenge is the modality differences between infrared and visible images for the VI-ReID task. Existing approaches try to design networks based on a single-stage training strategy to extract features. However, they often excessively rely on a particular feature, such as modality-specific features or modality-independent features, and overlook the significance of the diverse features obtained by combining them. To address this problem, we propose a diverse-feature collaborative progressive learning network (DCPLNet) for VI-ReID in this article. With the benefit of diverse information, our DCPLNet can effectively learn informative representations for reducing the modality differences. Specifically, we propose a novel three-stage progressive learning strategy (t-PLS) to progressively learn diverse features. For the proposed t-PLS, we design a contour feature enhancement module to mine human contour features and raise a perceptual contour feature loss for supervised feature extraction. Finally, we advance a batch adaptation module to establish feature links between samples. Extensive experiments on SYSU-MM01, RegDB, and LLCM datasets demonstrate that our proposed model performs better than most state-of-the-art methods.